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Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse.

ABSTRACT: INTRODUCTION:By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C"). AIM AND METHOD:The goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers. RESULTS:The predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials. CONCLUSION:These results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm. IMPLICATIONS:We assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

SUBMITTER: Frank DW

PROVIDER: S-EPMC7364823 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse.

Frank David W DW Cinciripini Paul M PM Deweese Menton M MM Karam-Hage Maher M Kypriotakis George G Lerman Caryn C Robinson Jason D JD Tyndale Rachel F RF Vidrine Damon J DJ Versace Francesco F

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 20200701 8

<h4>Introduction</h4>By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C").<h4>Aim and method</h4>The goal was to (1) build a classificatio ...[more]

PMID: 31724052

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Project description:IntroductionNegative affect has been identified as a factor influencing continued smoking during pregnancy. In this study, a multi-component emotion regulation intervention was developed to address negative emotional smoking triggers and pilot-tested among low-income pregnant smokers. Treatment feasibility and acceptability, cotinine-verified rates of smoking cessation, and self-report of mean cigarettes smoked were assessed.MethodsPregnant smokers who self-reported smoking in response to negative affect (N = 70) were randomly assigned to receive one of two 8-session interventions: (1) emotion regulation treatment combined with standard cognitive-behavioral smoking cessation (ERT + CBT) or (2) a health and lifestyle plus standard smoking cessation active control (HLS + CBT). Outcomes for the 4-month period following the quit date are reported.ResultsTreatment attendance and subjective ratings provide evidence for the feasibility and acceptability of the ERT + CBT intervention. Compared with the HLS + CBT control condition, the ERT + CBT condition demonstrated higher abstinence rates at 2 months (ERT + CBT = 23% vs. HLS + CBT = 0%, OR = 13.51; 95% CI = 0.70-261.59) and 4 months (ERT = 18% vs. HLS = 5%; OR = 2.98; 95% CI = 0.39-22.72) post-quit. Mean number of cigarettes per day was significantly lower in ERT + CBT at 2 months (ERT + CBT = 2.73 (3.35) vs. HLS + CBT = 5.84 (6.24); p = .05) but not at 4 months (ERT + CBT = 2.15 (3.17) vs. HLS + CBT = 5.18 (2.88); p = .06) post-quit.ConclusionsThe development and initial test of the ERT + CBT intervention supports its feasibility and acceptability in this difficult-to-treat population. Further development and testing in a Stage II randomized clinical trial are warranted.ImplicationsNegative affect has been identified as a motivator for continued smoking during pregnancy. To date, smoking cessation interventions for pregnant smokers have not specifically addressed the role of negative affect as a smoking trigger. This treatment development pilot study provides support for the feasibility and acceptability of a multi-component ERT + CBT for low-income pregnant smokers who self-report smoking in response to negative affect. Study findings support further testing in a fully-powered Stage II efficacy trial powered to assess mediators and moderators of treatment effects.

Project description:BACKGROUND:Advances in mobile communication technologies provide a promising avenue for the delivery of tobacco dependence treatment. Although mobile instant messaging (IM) apps (eg, WhatsApp, Facebook messenger, and WeChat) are an inexpensive and widely used communication tool, evidence on its use for promoting health behavior, including smoking cessation, is scarce. OBJECTIVE:This study aims to explore the perception of using mobile IM as a modality to deliver a proposed chat intervention for smoking cessation in community smokers in Hong Kong, where the proportion of smartphone use is among the highest in the world. METHODS:We conducted 5 focus group, semistructured qualitative interviews on a purposive sample of 15 male and 6 female current cigarette smokers (age 23-68 years) recruited from the community in Hong Kong. All interviews were audiotaped and transcribed. Two investigators independently analyzed the transcripts using thematic analyses. RESULTS:Participants considered mobile IM as a feasible and acceptable platform for the delivery of a supportive smoking cessation intervention. The ability to provide more personalized and adaptive behavioral support was regarded as the most valued utility of the IM-based intervention. Other perceived utilities included improved perceived psychosocial support and identification of motivator to quit. In addition, participants provided suggestions on the content and design of the intervention, which may improve the acceptability and usability of the IM-based intervention. These include avoiding health warning information, positive messaging, using former smokers as counselors, and adjusting the language style (spoken vs written) according to the recipients' preference. CONCLUSIONS:This qualitative study provides the first evidence that mobile IM may be an alternative mobile health platform for the delivery of a smoking cessation intervention. Furthermore, the findings inform the development of a chat-based, IM smoking cessation program being evaluated in a community trial.

Project description:Many lifelong smokers establish smoking habits during young adulthood. A university can be an effective setting for early smoking cessation. We evaluated long-term predictors of smoking cessation among smokers in a university setting.We longitudinally followed a cohort of smokers enrolled in a university smoking cessation program in Seoul, South Korea. Sociodemographic factors, smoking-related variables, and changes in smoking habits were assessed during 6-week visit sessions and follow-up telephone interviews conducted 1 year or more later.A total of 205 participants were followed up (mean follow-up duration: 27.1 months). Cessation rates were 47.3% at the end of the visit sessions and 28.8% at follow-up. The long-term persistent smoking rate was significantly higher among individuals with peers who smoked (odds ratio [OR] = 8.64; 95% confidence interval [CI] = 1.75, 42.80), with family members who smoked (OR = 3.28; 95% CI = 1.20, 9.00), and who smoked 10 to 19 cigarettes/day (OR?=?4.83; 95% CI?=?1.49, 15.69). Conversely, persistent smoking was less likely among those who attended the program regularly (OR?=?0.84 per visit; 95% CI?=?0.72, 0.99) and attempted quitting more frequently (OR?=?0.93 per attempt; 95% CI?=?0.87, 0.99). Use of smoking cessation medications (varenicline or bupropion) was not significantly associated with long-term quitting (OR?=?0.71; 95% CI?=?0.26, 1.93).Peer influences were the strongest predictors of failure in long-term cessation among smokers who attempted to quit. Similarly, the existence of smokers in the family was negatively associated with successful quitting. Regular attendance at a smoking cessation program and a high number of attempts to quit were positively associated with successful quitting. Targeting peer and family smoking groups together rather than targeting individual smokers alone, implementing active cessation programs encouraging regular attendance, and providing comprehensive antismoking environments might be effective strategies in a university setting.

Dataset Information

Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse.

Publications

Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse.

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